FrancescaDr

PhD in Computational Biology | MSc Computer Science & BSc Artificial Intelligence

Helmholtz Munich | TU Munich

Pinned Repositories

CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Language:Jupyter Notebook00
geome
Language:Python00
group-movie-recommendation
Group Movie Recommendation. Project made in the context of TU Delft's CS4056 Multimedia Search & Recommendation.
00
master-thesis-code
Language:Jupyter Notebook01
nsvki.nl
Language:HTML00
scMM
Language:Jupyter Notebook00
squidpy
Spatial Single Cell Analysis in Python
Language:Python00
Thesis-simulation-
Language:Jupyter Notebook00
squidpy
Spatial Single Cell Analysis in Python
Language:Python41575
MERFISH_analysis
Matlab software for the analysis of MERFISH data and the construction of MERFISH probes.
Language:MATLAB4432

FrancescaDr's Repositories

FrancescaDr/squidpy
Spatial Single Cell Analysis in Python
Language:Python
FrancescaDr/geome
Language:Python
FrancescaDr/master-thesis-code
Language:Jupyter Notebook1
FrancescaDr/CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
FrancescaDr/scMM
FrancescaDr/group-movie-recommendation
Group Movie Recommendation. Project made in the context of TU Delft's CS4056 Multimedia Search & Recommendation.
FrancescaDr/Thesis-simulation-
Language:Jupyter Notebook
FrancescaDr/nsvki.nl
Language:HTML